Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in people's life, such as monitoring security, autonomous driving and so on, ...with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning algorithms for detection tasks, the performance of object detectors has been greatly improved. In order to understand the main development status of object detection pipeline thoroughly and deeply, in this survey, we analyze the methods of existing typical detection models and describe the benchmark datasets at first. Afterwards and primarily, we provide a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors. Moreover, we list the traditional and new applications. Some representative branches of object detection are analyzed as well. Finally, we discuss the architecture of exploiting these object detection methods to build an effective and efficient system and point out a set of development trends to better follow the state-of-the-art algorithms and further research.
Hyper-heuristic approaches aim to automate heuristic design in order to solve multiple problems instead of designing tailor-made methodologies for individual problems. Hyper-heuristics accomplish ...this through a high-level heuristic (heuristic selection mechanism and an acceptance criterion). This automates heuristic selection, deciding whether to accept or reject the returned solution. The fact that different problems, or even instances, have different landscape structures and complexity, the design of efficient high-level heuristics can have a dramatic impact on hyper-heuristic performance. In this paper, instead of using human knowledge to design the high-level heuristic, we propose a gene expression programming algorithm to automatically generate, during the instance-solving process, the high-level heuristic of the hyper-heuristic framework. The generated heuristic takes information (such as the quality of the generated solution and the improvement made) from the current problem state as input and decides which low-level heuristic should be selected and the acceptance or rejection of the resultant solution. The benefit of this framework is the ability to generate, for each instance, different high-level heuristics during the problem-solving process. Furthermore, in order to maintain solution diversity, we utilize a memory mechanism that contains a population of both high-quality and diverse solutions that is updated during the problem-solving process. The generality of the proposed hyper-heuristic is validated against six well-known combinatorial optimization problems, with very different landscapes, provided by the HyFlex software. Empirical results, comparing the proposed hyper-heuristic with state-of-the-art hyper-heuristics, conclude that the proposed hyper-heuristic generalizes well across all domains and achieves competitive, if not superior, results for several instances on all domains.
A highly enantioselective dearomative 3+2 cycloaddition of benzothiazole has been successfully developed. A wide range of benzothiazoles and cyclopropane‐1,1‐dicarboxylates are suitable substrates ...for this reaction. The desired hydropyrrolo2,1‐bthiazole compounds were obtained in excellent enantioselectivity and yields (up to 97 % ee and 97 % yield). With the same catalytic system, a highly efficient kinetic resolution of 2‐substituted cyclopropane‐1,1‐dicarboxylates was also realized.
A highly enantioselective dearomative 3+2 cycloaddition of benzothiazole has been successfully developed. A wide range of benzothiazoles and cyclopropane‐1,1‐dicarboxylates are suitable substrates for this reaction. The desired hydropyrrolo2,1‐bthiazole compounds were obtained in excellent enantioselectivity and yields. With the same catalytic system, a highly efficient kinetic resolution of 2‐substituted cyclopropane‐1,1‐dicarboxylates was also realized.
Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An ...underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.
A DMAP‐N‐oxide, featuring an α‐amino acid as the chiral source, was developed, synthesized and applied in asymmetric Steglich rearrangement. A series of O‐acylated azlactones afforded C‐acylated ...azlactones possessing a quaternary stereocenter in high yields (up to 97 % yield) and excellent enantioselectivities (up to 97 % ee). Compared to the widespread use of pyridine nitrogen, which serves as the nucleophilic site in the asymmetric acyl transfer reaction, we discovered that chiral DMAP‐N‐oxides, in which the oxygen now acts as the nucleophilic site, are efficient acyl transfer catalysts. Our finding might open a new door for the development of chiral DMAP‐N‐oxides for asymmetric acyl transfer reactions.
A DMAP‐N‐oxide, featuring an α‐amino acid as the chiral source, was developed, synthesized and applied in asymmetric Steglich rearrangement. A series of O‐acylated azlactones afforded C‐acylated azlactones possessing a quaternary stereocenter in high yields (up to 97 % yield) and excellent enantioselectivities (up to 97 % ee).
Personnel scheduling: Models and complexity Brucker, Peter; Qu, Rong; Burke, Edmund
European journal of operational research,
05/2011, Letnik:
210, Številka:
3
Journal Article
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Due to its complexity, its challenging features, and its practical relevance, personnel scheduling has been heavily investigated in the last few decades. However, there is a relatively low level of ...study on models and complexity in these important problems. In this paper, we present mathematical models which cover specific aspects in the personnel scheduling literature. Furthermore, we address complexity issues by identifying polynomial solvable and NP-hard special cases.
Designing generic problem solvers that perform well across a diverse set of problems is a challenging task. In this work, we propose a hyper-heuristic framework to automatically generate an effective ...and generic solution method by utilizing grammatical evolution. In the proposed framework, grammatical evolution is used as an online solver builder, which takes several heuristic components (e.g., different acceptance criteria and different neighborhood structures) as inputs and evolves templates of perturbation heuristics. The evolved templates are improvement heuristics, which represent a complete search method to solve the problem at hand. To test the generality and the performance of the proposed method, we consider two well-known combinatorial optimization problems: exam timetabling (Carter and ITC 2007 instances) and the capacitated vehicle routing problem (Christofides and Golden instances). We demonstrate that the proposed method is competitive, if not superior, when compared to state-of-the-art hyper-heuristics, as well as bespoke methods for these different problem domains. In order to further improve the performance of the proposed framework we utilize an adaptive memory mechanism, which contains a collection of both high quality and diverse solutions and is updated during the problem solving process. Experimental results show that the grammatical evolution hyper-heuristic, with an adaptive memory, performs better than the grammatical evolution hyper-heuristic without a memory. The improved framework also outperforms some bespoke methodologies, which have reported best known results for some instances in both problem domains.
The enantioselective synthesis of carbocyclic nucleosides through the palladium‐catalyzed asymmetric allylic amination of alicyclic Morita‐Baylis‐Hillman (MBH) adducts with purines was successfully ...developed. With a combination of Pd2(dba)3/L7 as catalyst, various optically active carbocyclic nucleosides featuring a C=C double bond in the carbocycle moiety were obtained in high yields (up to 97%) with excellent N9/N7‐selectivities (>95/5) and enantioselectivities (up to >99.6%). In addition, these nucleoside analogs allowed for rapid transformation to a variety of other interesting structurally diverse chiral carbocyclic nucleosides.
In the context of workforce scheduling, there are many scenarios in which personnel must carry out tasks at different locations hence requiring some form of transportation. Examples of these type of ...scenarios include nurses visiting patients at home, technicians carrying out repairs at customers’ locations and security guards performing rounds at different premises, etc. We refer to these scenarios as workforce scheduling and routing problems (WSRP) as they usually involve the scheduling of personnel combined with some form of routing in order to ensure that employees arrive on time at the locations where tasks need to be performed. The first part of this paper presents a survey which attempts to identify the common features of WSRP scenarios and the solution methods applied when tackling these problems. The second part of the paper presents a study on the computational difficulty of solving these type of problems. For this, five data sets are gathered from the literature and some adaptations are made in order to incorporate the key features that our survey identifies as commonly arising in WSRP scenarios. The computational study provides an insight into the structure of the adapted test instances, an insight into the effect that problem features have when solving the instances using mathematical programming, and some benchmark computation times using the Gurobi solver running on a standard personal computer.
Hyper-heuristics are search methodologies that aim to provide high-quality solutions across a wide variety of problem domains, rather than developing tailor-made methodologies for each problem ...instance/domain. A traditional hyper-heuristic framework has two levels, namely, the high level strategy (heuristic selection mechanism and the acceptance criterion) and low level heuristics (a set of problem specific heuristics). Due to the different landscape structures of different problem instances, the high level strategy plays an important role in the design of a hyper-heuristic framework. In this paper, we propose a new high level strategy for a hyper-heuristic framework. The proposed high-level strategy utilizes a dynamic multiarmed bandit-extreme value-based reward as an online heuristic selection mechanism to select the appropriate heuristic to be applied at each iteration. In addition, we propose a gene expression programming framework to automatically generate the acceptance criterion for each problem instance, instead of using human-designed criteria. Two well-known, and very different, combinatorial optimization problems, one static (exam timetabling) and one dynamic (dynamic vehicle routing) are used to demonstrate the generality of the proposed framework. Compared with state-of-the-art hyper-heuristics and other bespoke methods, empirical results demonstrate that the proposed framework is able to generalize well across both domains. We obtain competitive, if not better results, when compared to the best known results obtained from other methods that have been presented in the scientific literature. We also compare our approach against the recently released hyper-heuristic competition test suite. We again demonstrate the generality of our approach when we compare against other methods that have utilized the same six benchmark datasets from this test suite.
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